Online learning for time series prediction

Oren Anava; Elad Hazan; Shie Mannor; Ohad Shamir

Online learning for time series prediction

Oren Anava, Elad Hazan, Shie Mannor, Ohad Shamir

Technion - Israel Institute of Technology

Research output: Contribution to journal › Conference article › peer-review

Abstract

We address the problem of predicting a time series using the ARMA (autoregressive moving average) model, under minimal assumptions on the noise terms. Using regret minimization techniques, we develop effective online learning algorithms for the prediction problem, without assuming that the noise terms are Gaussian, identically distributed or even independent. Furthermore, we show that our algorithm's performances asymptotically approaches the performance of the best ARMA model in hindsight.

Original language	English
Pages (from-to)	172-184
Number of pages	13
Journal	Journal of Machine Learning Research
Volume	30
State	Published - 2013
Event	26th Conference on Learning Theory, COLT 2013 - Princeton, NJ, United States Duration: 12 Jun 2013 → 14 Jun 2013

Keywords

Online learning
Regret minimization
Time series analysis

All Science Journal Classification (ASJC) codes

Software
Control and Systems Engineering
Statistics and Probability
Artificial Intelligence

Access to Document

https://proceedings.mlr.press/v30/Anava13.pdf

Cite this

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title = "Online learning for time series prediction",

abstract = "We address the problem of predicting a time series using the ARMA (autoregressive moving average) model, under minimal assumptions on the noise terms. Using regret minimization techniques, we develop effective online learning algorithms for the prediction problem, without assuming that the noise terms are Gaussian, identically distributed or even independent. Furthermore, we show that our algorithm's performances asymptotically approaches the performance of the best ARMA model in hindsight.",

keywords = "Online learning, Regret minimization, Time series analysis",

author = "Oren Anava and Elad Hazan and Shie Mannor and Ohad Shamir",

year = "2013",

language = "الإنجليزيّة",

volume = "30",

pages = "172--184",

journal = "Journal of Machine Learning Research",

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note = "26th Conference on Learning Theory, COLT 2013 ; Conference date: 12-06-2013 Through 14-06-2013",

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T1 - Online learning for time series prediction

AU - Anava, Oren

AU - Hazan, Elad

AU - Mannor, Shie

AU - Shamir, Ohad

PY - 2013

Y1 - 2013

N2 - We address the problem of predicting a time series using the ARMA (autoregressive moving average) model, under minimal assumptions on the noise terms. Using regret minimization techniques, we develop effective online learning algorithms for the prediction problem, without assuming that the noise terms are Gaussian, identically distributed or even independent. Furthermore, we show that our algorithm's performances asymptotically approaches the performance of the best ARMA model in hindsight.

AB - We address the problem of predicting a time series using the ARMA (autoregressive moving average) model, under minimal assumptions on the noise terms. Using regret minimization techniques, we develop effective online learning algorithms for the prediction problem, without assuming that the noise terms are Gaussian, identically distributed or even independent. Furthermore, we show that our algorithm's performances asymptotically approaches the performance of the best ARMA model in hindsight.

KW - Online learning

KW - Regret minimization

KW - Time series analysis

UR - http://www.scopus.com/inward/record.url?scp=84898039130&partnerID=8YFLogxK

M3 - مقالة من مؤنمر

SN - 1532-4435

VL - 30

SP - 172

EP - 184

JO - Journal of Machine Learning Research

JF - Journal of Machine Learning Research

T2 - 26th Conference on Learning Theory, COLT 2013

Y2 - 12 June 2013 through 14 June 2013

ER -

Online learning for time series prediction

Abstract

Keywords

All Science Journal Classification (ASJC) codes

Access to Document

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